Neural prediction of missile dynamics during hardware-in-the-loop captive-carry experiments

The Naval Research Laboratory's P-3 captive-carry correlation algorithms combine hardware-in-the-loop missile simulator closed-loop anechoic chamber results and open-loop field test results to calculate a single measure of electronic attack effectiveness (miss distance). Prediction of the missile dynamics from the open-loop captive-carry seeker response represents an important component of these algorithms. This paper reports the use of neural networks to predict the missile dynamics by training Levenburg-Marquardt type predictors with the closed-loop seeker azimuth, elevation and range as inputs, the change in position of the missile in spherical coordinates (relative to the missile body), and the missile yaw and pitch as outputs. The trained networks are then used to process the open-loop seeker information to predict the dynamics. The performance of the predictors is evaluated numerically using recent test results. The prediction error is also quantified